Big data-driven Clock Synchronization Detection Approach in Smart Grid Using Fuzzy Neural Network

The sequence of events (SOE) recording is an important information for analyzing power system faults. If the clock system in the substation is not synchronized properly, it can lead to errors in SOE recording time, affecting the analysis and judgment of faults. On the basis of analyzing the implementation method of clock synchronization in the scheduling automation system, this article proposes a method based on fuzzy neural network for detecting synchronization anomalies in the substation clock system. This method can detect anomalies in the clock synchronization system in the substation at the first time. The experiment shows that this method can greatly improve the timeliness of synchronous fault detection and fault handling efficiency in the substation clock system, further enhancing the reliability of the clock synchronization system and substation automation system.